Patent SG-11201901334S-A (SG)
Title: Convolutional neural network-based mode selection and defect classification for image fusion
Inventor: BRAUER BJORN
IPC: H01L21/66
Filed: 2017-09-13
Abstract: INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property 1111111111111111111111111111111111111111111111001111110111111111111111111111111 Organization International Bureau (10) International Publication Number (43) International Publication Date .....0\"\" WO 2018/053031 Al 22 March 2018 (22.03.2018) WIP0 I PCT (51) International Patent Classification: (74) Agent: MCANDREWS, Kevin et al.; KLA-Tencor Corpo- HO1L 21/66 (2006.01) ration, Legal Department, One Technology Drive, Milpitas, (21) International Application Number: California 95035 (US). PCT/US2017/051405 (81) Designated States (unless otherwise indicated, for every (22) International Filing Date: kind of national protection available): AE, AG, AL, AM, 13 September 2017 (13.09.2017) AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, (25) Filing Language: English DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, (26) Publication Language: English HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, (30) Priority Data: MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, 62/394,744 14 September 2016 (14.09.2016) US OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, 15/371,882 07 December 2016 (07.12.2016) US SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (71) Applicant: KLA-TENCOR CORPORATION [US/US]; Legal Department, One Technology Drive, Milpitas, Cali- (84) Designated States (unless otherwise indicated, for every fornia 95035 (US). kind of regional protection available): ARIPO (BW, GH, GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, (72) Inventor: BRAUER, Bjorn; 16698 NW Tucson Street, UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, Beaverton, OR 97006 (US). TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, _ (54) Title: CONVOLUTIONAL NEURAL NETWORK-BASED MODE SELECTION AND DEFECT CLASSIFICATION FOR = IMAGE FUSION = — = — Computer subsystem(s) 102 = = = — = Computer subsystem 36 = = = = = = 28 ---\ 26 - \1=1 20 24 % 34 /10 16 m sr_7 18 21 -\ 1 ilk 32 = 1 30 \1 4 -,„,_ 22 1 I Il Fig. 2 1-1 C c:::: ) (57) : Systems and methods for classifying defects using hot scans and convolutional neural networks (CNNs) are disclosed. MPrimary scanning modes are identified by a processor and a hot scan of a wafer is performed. Defects of interest and nuisance data are 0 Ur 4 selected and images of those areas are captured using one or more secondary scanning modes. Image sets are collected and divided into ----. subsets. CNNs are trained using the image subsets. An ideal secondary scanning mode is determined and a fmal hot scan is performed. GC 1-1 Defects are filtered and classified according to the fmal hot scan and the ideal secondary scanning mode CNN. Disclosed systems © for classifying defects utilize image data acquisition subsystems such as a scanning electron microscope as well as processors and N electronic databases. o [Continued on next page] WO 2018/053031 Al MIDEDIMOMBIREIHRIEWIIMIMIOMOIIINVOIMIE EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, SK, SM, GQ, GW, KM, ML, MR, NE, SN, TD, TG). Published: — with international search report (Art. 21(3))